Polygon creation for an aquatic geographic information system

ABSTRACT

A method of processing geo-statistical data includes piloting a watercraft with a monitoring system on a water body, taking measurements of a depth of the water body and the position of the watercraft using the monitoring system, and aligning the depth measurements with the position measurements. The method also includes creating a contour map from the depth and position measurements, creating a polygon on the contour map, and analyzing at least one of the depth and position measurements within the polygon.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to U.S. patent application Ser. No.13/948,904, filed on Jul. 23, 2013, and entitled “AQUATIC GEOGRAPHICINFORMATION SYSTEM,” which claims priority to U.S. Provisional PatentApplication No. 61/675,304, filed on Jul. 24, 2012, and entitled“AQUATIC GEOGRAPHIC INFORMATION SYSTEM,” the disclosures of which areincorporated by reference in their entirety.

This application is also related to U.S. patent application Ser. No.14/673,267 filed on Mar. 30, 2015, and entitled “REPORTING FOR ANAQUATIC GEOGRAPHIC INFORMATION SYSTEM”; U.S. patent application Ser. No.14/673,318 filed on Mar. 30, 2015, and entitled “CONTOUR INTERVALCONTROL FOR AQUATIC GEOGRAPHIC INFORMATION SYSTEM”; U.S. patentapplication Ser. No. 14/673,406 filed on Mar. 30, 2015, and entitled“ELEVATION ADJUSTMENT FOR AN AQUATIC GEOGRAPHIC INFORMATION SYSTEM”; andU.S. patent application Ser. No. 14/673,459 filed on Mar. 30, 2015, andentitled “TRIP REPLAY FOR AN AQUATIC GEOGRAPHIC INFORMATION SYSTEM”.

BACKGROUND

Geographic information systems (GIS) are used to manage many types ofinformation about the earth. Data points representing information suchas altitude or plant growth can be mapped using global positioningsystem data to create layers in a GIS. GIS can even be used to analyzeareas that are covered with water, such as aquatic environments. GIS canget input from many different sources, including aerial photographs andacoustic sounders. In this manner, data can be organized and mapped tospecific areas of the planet.

Depth finders/acoustic sounders mounted on watercraft are often used byscientists and sportsmen/women for various purposes. For example, ascientist may want to detect and measure aquatic plant growth in a lake.For another example, an angler may want to find fish in a river oridentify trends in each item located by sounding. A typical depth finderdisplay shows the depth of the water beneath the boat and possiblyinformation regarding what is to the sides of the boat. This informationis only displayed for a short period of time, as the display isconstantly being updated with new data. While depth finder data can beused to create a GIS layer, the data collected by a depth finder dependson the path taken by the boat. This data is not easily entered into GISsoftware that stores data according to absolute coordinates.

SUMMARY

According to one embodiment of the present invention, a method ofprocessing geo-statistical data includes piloting a watercraft with amonitoring system on a water body, taking measurements of a depth of thewater body and the position of the watercraft using the monitoringsystem, and aligning the depth measurements with the positionmeasurements. The method also includes creating a contour map from thedepth and position measurements, creating a polygon on the contour map,and analyzing at least one of the depth and position measurements withinthe polygon.

In another embodiment, a geographic information system includes amonitoring system having a depth measurement device for measuring adepth of a water body and a position measurement device for measuring aposition of the monitoring system wherein the monitoring system isconfigured to record depth data points and coordinate data points. Thesystem also includes a data link connected to the monitoring system, anetwork connected to the data link, a server connected to the network, adatabase connected to the network, and a user computer connected to thenetwork. The server is configured to receive the depth data points andthe coordinate data points, align the depth data points with thecoordinate data points, and create a contour map of the water body fromthe depth data points and the coordinate data points. There is also apolygon created by the user, the polygon being closed and positioned onthe contour map.

In another embodiment, a method of processing geo-statistical dataincludes a server receiving depth data points that represent values of awater body depth and coordinate data points that represent thecoordinates from which the depth data points were measured from a waterbody monitoring system. The server also aligns the depth data pointswith the coordinate data points, extracts the depth data points and thecoordinate data points, and creates a contour map from the depth datapoints and the coordinate data points. The method also includes creatinga closed polygon on the contour map.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram showing architecture of an automaticaquatic geographic information system (GIS).

FIG. 2 shows an automatically generated output report from the GISSystem.

FIG. 3 shows a flow chart of automated processing of geo-statisticaldata.

FIG. 4A shows a flow chart of automated contour map generation forgeo-statistical data.

FIG. 4B shows a flow chart of automated vegetation map generation forgeo-statistical data.

FIG. 4C shows a flow chart of automated substrate map generation forgeo-statistical data.

FIG. 4D shows a flow chart of automated sonar imagery generation forgeo-statistical data.

FIG. 4E shows a flow chart of automated report generation forgeo-statistical data.

FIG. 5 shows a report generated from the GIS System including anautomated and interactive contour interval control.

FIG. 6A shows a report generated from the GIS System for a lake that hasbeen partially traversed.

FIG. 6B shows a portion of a report generated from the GIS Systemincluding a zoomed view and higher resolution of the report.

FIG. 7 shows a report generated from the GIS System including automatedaltitude adjustment and data offset.

FIG. 8A shows a trip replay generated from the GIS System with depthinformation.

FIG. 8B shows a trip replay generated from the GIS System withvegetation information.

FIG. 9 shows a flow chart of automated depth adjustment forgeo-statistical data based on tidal data.

DETAILED DESCRIPTION

In FIG. 1, architecture of an aquatic geographic information system(GIS) 20 is shown. In FIG. 2, an example report 42A from GIS 20 isshown. FIGS. 1-2 will now be discussed simultaneously.

In the illustrated embodiment, GIS 20 includes monitoring system 22,network 24, server 26, database 28, user computers 30A-30B, and users32A-32B. Monitoring system 22 is mounted on watercraft 34, such as aboat, that can be piloted on water body 36, such as a lake, river,ocean, reservoir, etc. Monitoring system 22 includes a clock, a globalpositioning system (GPS) unit, a thermometer, and a sonar unit.

Monitoring system 22 has data link 38 that connects monitoring system toservice provider 40. Data link 38 can comprise one of the many knowndata link types, such as a cellular telephone network, a satellitenetwork, a short-range wireless connection, or a hardwired connection,among other things. Service provider 40 is connected to network 24, suchas the internet. Server 26 is connected to network 24, and server 26 isalso connected to database 28. In addition, there is a plurality of usercomputers 30A-30B connected to network 24, with each user computer30A-30B having a user 32A-32B, respectively.

As watercraft 34 is driven by user 32A along pathway 44 on water body36, monitoring system 22 takes a series of measurements (called “pings”or “data points”) of various parameters and records them with atimestamp that includes the date down to the microsecond level. In theillustrated embodiment, these parameters can include, but are notlimited to, location, water temperature, water depth, plant height, andbottom hardness/softness. The pings are sent through data link 38,service provider 40, and network 24 in order to reach server 26. As willbe explained later in greater detail with FIG. 2, server 26 compiles thepings into a single image automatically. Each image is then entered intodatabase 28 and is associated with a user identifier, a trip identifier,and a water body identifier.

In order for user 32A to retrieve the images stored on database 28, user32A must first be authenticated by server 26. Once server 26 issatisfied that user 32A is in fact user 32A, server 26 authorizes user32A to gain access to particular entries on database 28. For example,user 32A may be granted access to his/her own entries. For anotherexample, user 32A and user 32B can agree to share data, whereby server26 groups the access rights for user 32A with the access rights for user32B. Thereby, user 32A can access user 32B's entries and user 32B canaccess user 32A's entries. Although each user 32A-32B can decide onhis/her own whether to join a group in order to share data or keephis/her data to him/herself.

In addition, multiple users 32 that are part of the same group canupload images to server 26 of the same water body 36. In this scenario,server 26 merges the images into a single database entry image of waterbody 36. In such a function the data points and images do not need to bereprocessed, instead the data points there are combined and thenprocessed together.

After server 26 has processed an image from user 32A, report 42A is sentto user computer 30A. In general, report 42A includes informationregarding the parameters of water body 36 and of the trip itself. Morespecifically, report 42A can include statistics about an image such as:total number of pings processed; data collector GPS references; filetypes; trip conditions; collection data set; raw data; transect lengths46 (the distances between adjacent passes of pathway 44); and moredetailed analysis of transect lengths 46. Report 42A can also include adata layer from a processed image that is superimposed over an aerialview of water body 36. Such a data layer can include data analysisoutput regarding: percent of water body 36 traversed; total percent ofwater body 36 traversed (for merged images); water depths; plant percentbiovolume (which relates to how much of the water in water body 36 isoccupied by plants); total plant percent biovolume; correlation betweenwater depth and plant percent biovolume; water temperatures; manual dataentry points (for example, an area of 100% biovolume that could not betraversed by watercraft 34). The processed output in report 42A iscreated using a uniform set of parameters. Thereby, report 42A can bedirectly compared to report 42B even if report 42B.

The components and configuration of GIS 20 as shown in FIGS. 1-2 allowfor the measuring, transmission, processing, storage, and reviewing ofgeographic data, specifically data related to bodies of water. Suchmeasurement of bodies of water can be crowdsourced, meaning that if user32A can collects information from one-half of a particular water body 36and user 32B collects information from the other half of that same waterbody 36, both users 32A-32B will have data for the entire water body 36.Similarly, if multiple users 32 share information about multiple waterbodies 36, every user 32 does not need to personally measure each waterbody 36 to gain information about all of the water bodies 36.Alternatively, user 32A and user 32B can each have private informationabout the same water body 36 if users 32A-32B would so prefer. Inaddition, report 42A regarding water body 36 can be used to establishbaseline to which report 42B can be compared. This is especially usefulif the information for report 42B is collected at a later time or from adifferent water body 36 than that of report 42A.

Illustrated in FIGS. 1-2 is one embodiment of the present invention, towhich there are alternative embodiments. For example, GIS 20 can measureand process other types of data, such as barometric pressure or biomass.

In FIG. 3, a flow chart showing automated processing 100 ofgeo-statistical data is shown. In the illustrated embodiment, server 26(shown in FIG. 1) prepares a sonar log containing pings that was createdby monitoring system 22 (shown in FIG. 1) at step 102. At this step, thesonar log is read and checked for validity. At step 104, acoustic datapoints and coordinate data points are extracted and aligned, which isthe first level of summarization of the data from monitoring system 22.Then, the coordinate data is statistically aggregated, cleaned, andvalidated at step 106. The data is also geospatially validated at step108. Then at step 109, the depth values of the data are adjusted, ifnecessary. Finally, at step 110 an output is created as the second levelof summarization of the data, and a notification is sent to usercomputer 30A (shown in FIG. 1). The output of automated processing 100will be discussed later with FIGS. 4A-4E, although in general, theoutput can include a contour map, a vegetation map, a substrate orbottom hardness map, a sonar image, or a report 42A.

The steps of automated processing 100 as shown in FIG. 3 allow forparameters to be measured by user 32A and report 42A to be createdwithout requiring user 32A to manually convert the sonar data andcoordinate data into an output.

In FIG. 4A, a flow chart of automated contour map generation 200 forgeo-statistical data is shown. Specifically, depth data is being outputin automated contour map generation 200 that creates a topographicalrepresentation of the bottom of water body 36 (shown in FIG. 1). In theillustrated embodiment, server 26 (shown in FIG. 1) formats thecoordinate data at step 202. The coordinate data is output in decimaldegrees format without a map. At step 204, a Universal TransverseMercator (UTM) contour map is created using the coordinate data thatallows for the data to be exported or displayed without a background. Atstep 206, a Mercator contour map is created that can be displayed over abackground, such as an aerial photograph of water body 36 or anothermap.

In FIG. 4B, a flow chart of automated vegetation map generation 300 forgeo-statistical data is shown. Specifically, vegetation data is beingoutput in automated vegetation map generation 300. In the illustratedembodiment, server 26 (shown in FIG. 1) formats and analyzes thecoordinate data at step 302. The coordinate data is output in decimaldegrees format without a map at step 304. At step 306, a UTM contour mapis created using the coordinate data that allows for the data to beexported or displayed without a background. At step 308, a Mercatorcontour map is created that can be displayed over a background, such asan aerial photograph of water body 36 (shown in FIG. 1) or another map.

In FIG. 4C, a flow chart of automated substrate map generation 400 forgeo-statistical data is shown. Specifically, substrate or bottomhardness data is being output in automated substrate map generation 400.In the illustrated embodiment, server 26 (shown in FIG. 1) formats andanalyzes the coordinate data at step 402. The coordinate data is outputin decimal degrees format without a map at step 404. At step 406, a UTMcontour map is created using the coordinate data that allows for thedata to be exported or displayed without a background. At step 408, aMercator contour map is created that can be displayed over a background,such as an aerial photograph of water body 36 (shown in FIG. 1) oranother map.

In FIG. 4D, a flow chart of automated sonar imagery generation 500 forgeo-statistical data is shown. Specifically, a sonar image is beingoutput in automated sonar imagery generation 500. In the illustratedembodiment, server 26 (shown in FIG. 1) reads and cleans the sonar logpings at step 502. At step 504, an image is generated by addingindividual pixel widths that are themselves generated at step 506. Ifnecessary, an extremely long sonar image can be created by addingmultiple sonar images together (not shown).

In FIG. 4E, a flow chart of automated report generation 600 forgeo-statistical data is shown. Specifically, the outputs of automatedreport generation 600 can include measured or calculated parameters aswell as further processed outputs of automated generations 300, 400,500, and/or 600. For example, at step 602, average depth, percent ofarea covered by plants, and average hardness can be calculated.Furthermore, statistical correlations of parameters such as vegetationbiovolume or substrate hardness can be made at each contour level (i.e.at each depth range). For another example, at step 604, imagery displayinformation is created, such as an overlay of the outputs of automatedgenerations 300, 400, 500, and/or 600 upon an aerial photograph of waterbody 36 (shown in FIG. 1). Further, also at step 604, graphical displayinformation can be created, including visual representations of theoutputs generated at step 602.

The steps of automated contour map generation 200, automated vegetationmap generation 300, automated substrate map generation 400, automatedsonar imagery generation 500, and automated report generation 600 asshown in FIGS. 4A-4E, respectively, allow for the data collected bymonitoring system 22 (shown in FIG. 1) to be visualized and used in ameaningful way by at least user 32A (shown in FIG. 1). This feat isaccomplished without requiring much if any work to be done by user 32Ahim/herself beyond collecting data with monitoring system 22.

In FIG. 5, report 42C generated from GIS 20 including contour intervalcontrol 700 is shown. When server 26 (shown in FIG. 1) performsautomated contour map generation 200 (shown in FIG. 4A), a plurality ofreports 42C are made and stored in database 28 (shown in FIG. 1). Eachof the plurality of reports 42C has a different depth range at whichcontours 702 are placed to represent topographical changes in depth. Inthe illustrated embodiment, the depth range is 0.91 meters (3 feet),meaning that a contour 702 is placed where the depth is 3 feet, 6 feet,9 feet, etc. This is in contrast to report 42A (shown in FIG. 2) wherethe depth range is 0.30 meters (1 foot). This is evidenced by fewercontours 702 existing in report 42C than in report 42A.

User 32A can select which depth range is most desirable, and server 26(shown in FIG. 1) will send the corresponding report 42. Which report 42is most desirable can be dependent on how large water body 36 is and howclose user 32A has zoomed in on report 42. If the depth range is shallowand the view of a report 42 is fully zoomed out, there may be too manycontour lines 702 that are crowded together. This can destroy theusefulness of a report 42. Some exemplary, non-limiting depth rangesthat server 26 can create are 1 foot, 3 feet, 5 feet, and 10 feet.

In FIG. 6A, report 42D generated from GIS 20 for water body 36 that hasbeen partially traversed is shown. In FIG. 6B, a portion of report 42Dgenerated from GIS 20 including user-created polygon 800 is shown. Itshould be noted that if user 32A (shown in FIG. 1) had traversed pathway44, if user 32B (shown in FIG. 1) had traversed the remainder of waterbody 36, and if users 32A-32B were grouped together, the merging oftheir data would produce allow for the analysis of the entirety of waterbody 36. (Although it would be best if users 32A-32B performed theirdata collection close in time to prevent the seasonal cycles of plantgrowth from rendering a combination of the data misleading.)

On the other hand, user 32A can analyze a subset of the data in report42D. This is accomplished by creating polygon 800. Polygon 800 iscomprised of a plurality of straight edges 802 that form a closed shape.Within polygon 800, server 26 (shown in FIG. 1) can perform at least aportion of automated report generation 600 (shown in FIG. 4E). Forexample, using depth data, the total volume of water located withinpolygon 800 can be calculated, as could average percent biovolume.

In FIG. 7, report 42E generated from the GIS including automatedaltitude adjustment and data offset is shown. In the illustratedembodiment, one of the parameters monitored by monitoring system 22(shown in FIG. 1) can include elevation (which is a component of the GPSlocation). While any individual measurement of elevation along pathway900 may deviate from the actual elevation, the average elevationcollected at each ping can give a very accurate value for elevation(given that the water in water body 36 is substantially flat) that canbe indicated in report 42E. If there were another data set to be mergedwith the data preceding report 42E, the average elevation of that dataset can be calculated. Thereby, the difference of the two averageelevations can be calculated. Then this value can either be subtractedfrom every depth value of the higher one or added to every depth valueof the lower one to simulate both data sets being measured at the samewater level in water body 36. This would allow the two data sets to bemerged even if the water level in water body 36 had greatly fluctuatedbetween the time the first data set was created and the time the seconddata set was created. Such a depth adjustment process can occur, forexample, at step 109 (shown in FIG. 3) and can be performed by, forexample, monitoring system processor 26 (shown in FIG. 1).

Such a merging of data can occur using external data, such as in thecase of a reservoir drawdown. In this instance, the known drawdown levelcould be added or subtracted from the depth data of one of the data setsin order to merge the two.

In addition, a drawdown of a known magnitude can be simulated in report42E. The data for report 42E was originally gathered when the entiretyof the land under pathway 900 was under water body 36. During thegeneration of report 42E, all of the depth data has a certain valueadded or subtracted from it. This can be used to compensate for how farbelow the waterline the sonar unit is located on watercraft 34 (shown inFIG. 1). In the illustrated embodiment, this data offset is used tosimulate water body 36 having a substantially lowered water level.Report 42E shows a plurality of sandbars 902A-902E (shown in green)wherein the land formerly under water body 36 would be exposed. This canbe a useful navigational tool to indicate that pathway 900 would nolonger be an acceptable route to take if the water level of water body36 were to reach (or in some cases, merely approach) the simulated waterlevel in report 42E.

In FIG. 8A, trip replay 1000A generated from GIS 20 with depthinformation is shown. In FIG. 8B, trip replay 1000B generated from GIS20 with vegetation information is shown. While FIGS. 8A-8B are similar,FIG. 8A includes depth contours without vegetation data while FIG. 8Bincludes vegetation data.

When server 26 (shown in FIG. 1) performed automated processing 100(shown in FIG. 3) sonar data was aligned with coordinate data. Thereby,when automated contour map generation 200 (shown in FIG. 4A) isperformed, trip replay 1000A can be created. (Similarly, when automatedsonar imagery generation 500 is performed (shown in FIG. 4D), tripreplay 1000B can be created.) In the illustrated embodiment, sonardisplay 1002A appears on the right side of trip replay 1000A, and mapdisplay 1004A appears on the left side of trip replay 1000A. In general,sonar display 1002A is contemporaneously coordinated with map display1004A. More specifically, sonar pings down indicating line 1006A shownin sonar display 1002A occurred at the location of indicating point1008A on map display 1004A.

An entire trip along pathway 1010A can be illustrated in trip replay1000A, with sonar display 1002A, indicating line 1006A, map display1004A, and indicating point 1008A moving progressively together. Thisallows for user 32A (shown in FIG. 1) to watch the entire trip alongpathway 1010A in order to verify that the output from automated contourmap generation 200 matches what is indicated by the sonar data.

In FIG. 9, a flow chart of one embodiment of automated depth adjustmentstep 109 for geo-statistical data based on tidal data is shown. Thedepth adjustment process can be performed by, for example, monitoringsystem processor 26 (shown in FIG. 1).

At step 1100, the sonar log pings are read and converted to summarycoordinates. At step 1102, the geospatial center of the cumulativecoordinates is found, and the primary water body where most of thecoordinates exist is found at step 1104. At step 1106 it is determinedwhether there are any tidal stations assigned to this primary waterbody. If there are none, then step 109 can be completed and dataprocessing can continue at step 110 (shown in FIG. 3). This would be thecase where the primary water body is an inland lake, river, or stream.

On the other hand, if there is a tidal station assigned to the primarywater body, then all the tidal stations assigned to the primary waterbody are loaded at step 1108. At step 1110, the closest tidal station isfound using the geospatial center of the coordinates found in step 1102.At step 1112, one hour is subtracted from the start time of the sonarlog, and one hour is added to the end time of the sonar log at step1114. Then the predictive tidal data from the closest tidal station isloaded between the times calculated in steps 1112 and 1114 in one minuteincrements. The depth data for each sonar log coordinate is compared tothe predictive tidal data and the Mean Lower Low Water (MLLW) offset infeet is applied (i.e. added or subtracted) at step 1108. This occursindividually at each depth data point and the amount of correction toapply depends on the time (i.e. the particular minute) that the datapoint was measured. At step 1120, the tidal station, tidal adjustment,and adjusted depth for each coordinate data point is recorded indatabase 28 (shown in FIG. 1).

Illustrated in FIG. 9 is one embodiment of automated depth adjustmentstep 109, for which there are alternative embodiments. For example,multiple tidal stations can be used can be used to adjust different datapoints within the data set depending on their respective locations. Foranother example, geospatial and directional calculations can be made todetermine the flow of the tide using three or more tidal stations, whichcan increase the accuracy of the depth adjustment for each data point.For a further example, actual tidal data can be used instead ofpredictive tidal data for stations that measure actual tidal data.

It should be recognized that the present invention provides numerousbenefits and advantages. For example, GIS 20 data can be processedautomatically such that it can be layered on top of a map. For anotherexample, outputs that are automatically generated can be verified by auser with the sonar image, which increases the scientific confidence inthe outputs.

Further information can be found in U.S. patent application Ser. No.12/784,138, entitled “SYSTEMS, DEVICES, METHODS FOR SENSING ANDPROCESSING FISHING RELATED DATA,” filed May 20, 2010, by Lauenstein etal., which is herein incorporated by reference.

DESCRIPTION OF POSSIBLE EMBODIMENTS

The following are non-exclusive descriptions of possible embodiments ofthe present invention.

A geographic information system according to an exemplary embodiment ofthis disclosure, among other possible things comprises: a server that isconnected to a network; a database connected to the server; and aplurality of database entries, each database entry comprising: anidentifier; and a plurality of data points representing a water bodyparameter; wherein the database is accessible by an authenticated userand wherein the user can access a select group of the plurality ofdatabase entries.

The geographic information system of the preceding paragraph canoptionally include, additionally and/or alternatively, any one or moreof the following features, configurations, and/or additional components:

A further embodiment of the foregoing geographic information system,wherein the identifier can include a user identifier, a trip identifier,and a water body identifier.

A geographic information system according to an exemplary embodiment ofthis disclosure, among other possible things comprises: a server that isconnected to a network; a database connected to the server; a firstdatabase entry comprising: a first identifier; and a first plurality ofdata points representing a water body parameter; and a second databaseentry comprising: a second identifier; and a second plurality of datapoints representing a water body parameter; wherein the server combinesthe first and second pluralities of data points in order to process thefirst and second pluralities of data points.

The geographic information system of the preceding paragraph canoptionally include, additionally and/or alternatively, any one or moreof the following features, configurations, and/or additional components:

A further embodiment of the foregoing geographic information system cancomprise: a third database entry that includes the first and secondpluralities of data points wherein the server processes the thirddatabase entry.

A method of processing geo-statistical data according to an exemplaryembodiment of this disclosure, among other possible things, comprises:preparing a data log; extracting acoustic data and coordinate data fromthe data log; aligning the acoustic data and the coordinate data;cleaning and aggregating the coordinate data; validating the coordinatedata geospatially; and creating an output.

The method of the preceding paragraph can optionally include,additionally and/or alternatively, any one or more of the followingfeatures, configurations, and/or additional components:

A further embodiment of the foregoing method, wherein the output can bea contour map.

A method of reporting geo-statistical data according to an exemplaryembodiment of this disclosure, among other possible things, comprises:providing a contour map of a water body having a plurality of depthranges; correlating a water body parameter to at least one of the depthranges.

The method of the preceding paragraph can optionally include,additionally and/or alternatively, any one or more of the followingfeatures, configurations, and/or additional components:

A further embodiment of the foregoing method can comprise: correlating awater body parameter to each depth range.

A method of selecting data presentation according to an exemplaryembodiment of this disclosure, among other possible things, comprises:preparing a data log; extracting depth data and coordinate data from thedata log; aligning the depth data and the coordinate data; cleaning andaggregating the coordinate data; validating the coordinate datageospatially; creating a first contour map with a first plurality ofdepth ranges from the coordinate data; and creating a second contour mapwith a second plurality of depth ranges.

The method of the preceding paragraph can optionally include,additionally and/or alternatively, any one or more of the followingfeatures, configurations, and/or additional components:

A further embodiment of the foregoing method, wherein the firstplurality of depth ranges can be differentiated by 0.30 meters and thesecond plurality of depth ranges can be differentiated by 0.91 meters.

A method of measuring using data according to an exemplary embodiment ofthis disclosure, among other possible things, comprises: preparing adata log; extracting acoustic data and coordinate data from the datalog; aligning the acoustic data and the coordinate data; creating acontour map with the acoustic data and the coordinate data; creating apolygon on the contour map; analyzing at least one of the acoustic dataand the coordinate data within the polygon.

A method of adjusting altitude data according to an exemplary embodimentof this disclosure, among other possible things, comprises: preparing adata log; extracting altitude data and coordinate data from the datalog; aligning the altitude data and the coordinate data; cleaning andaggregating the coordinate data; averaging the altitude data to obtainan average altitude; and replacing the altitude data with the averagealtitude at each coordinate.

A method of adjusting altitude data according to an exemplary embodimentof this disclosure, among other possible things, comprises: preparing adata log; extracting altitude data and coordinate data from the datalog; aligning the altitude data and the coordinate data; cleaning andaggregating the coordinate data; changing the altitude at eachcoordinate by a given value.

A method of replaying measured data according to an exemplary embodimentof this disclosure, among other possible things, comprises: preparing adata log using measured parameters that were measured along a pathway;extracting acoustic data and coordinate data from the data log; aligningthe acoustic data and the coordinate data; creating a contour mapincluding the pathway taken while measuring the parameters; creating asonar image from the acoustic data; and displaying simultaneously thecontour map and the sonar image.

The method of the preceding paragraph can optionally include,additionally and/or alternatively, any one or more of the followingfeatures, configurations, and/or additional components:

A further embodiment of the foregoing method can further comprise:indicating a first position along the sonar image; and indicating asecond position along the pathway that is aligned with the firstposition along the sonar image.

While the invention has been described with reference to an exemplaryembodiment(s), it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment(s) disclosed, but that theinvention will include all embodiments falling within the scope of theappended claims.

The invention claimed is:
 1. A method of processing geo-statisticaldata, the method comprising: piloting a first watercraft on a waterbody, the first watercraft including a first monitoring system; taking afirst plurality of measurements of a depth of the water body using thefirst monitoring system; taking a second plurality of measurements of aposition of the first watercraft using the first monitoring system;taking a third plurality of measurements of a water body parameter ofthe water body using the first monitoring system; aligning the firstplurality of measurements with the second plurality of measurements;aligning the third plurality of measurements with the second pluralityof measurements; creating a contour map from the first plurality ofmeasurements and the second plurality of measurements; creating apolygon on the contour map; analyzing at least one of the firstplurality of measurements and the second plurality of measurementswithin the polygon; and analyzing the third plurality of measurementswithin the polygon.
 2. The method of claim 1, wherein the water bodyparameter describes one of water temperature, barometric pressure, plantheight, plant biovolume, area covered by plants, biomass, substratehardness/softness, and bottom hardness/softness.
 3. The method of claim1, wherein the contour map includes a plurality of depth ranges and themethod further comprises: correlating the water body parameter to atleast one of the depth ranges to create a report.
 4. The method of claim1, wherein contour map is additionally created from a fourth pluralityof measurements of the depth of the water body from a second watercraftusing a second monitoring system and a fifth plurality of measurementsof measurements of a position of the second watercraft using the secondmonitoring system.
 5. The method of claim 1, wherein the polygon iscomprised of a plurality of straight edges.
 6. The method of claim 1,creating a report that includes contour map with the polygon.
 7. Themethod of claim 1, wherein analyzing includes calculating a total volumeof water within the polygon.
 8. A geographic information systemcomprising: a monitoring system comprising: a depth measurement devicefor measuring a depth of a water body wherein the monitoring system isconfigured to record depth data points; and a position measurementdevice for measuring a position of the monitoring system wherein themonitoring system is configured to record coordinate data points; awater body parameter measurement device for measuring a parameter of thewater body, wherein the monitoring system is configured to recordparameter data points; a data link connected to the monitoring system; anetwork connected to the data link; a server connected to the network; adatabase connected to the network; and a user computer connected to thenetwork; wherein the server is configured to receive the depth datapoints and the coordinate data points, align the depth data points withthe coordinate data points, create a contour map of the water body fromthe depth data points and the coordinate data points; a polygon createdby the user, the polygon being closed and positioned on the contour map;and a report created by the server, the report including an analysis ofthe water body within the polygon of the parameter data points.
 9. Thegeographic information system of claim 8, wherein the server is furtherconfigured to analyze at least one of the depth data points and thecoordinate data points within the polygon.
 10. The geographicinformation system of claim 8, wherein the polygon is comprised of aplurality of straight edges.
 11. The geographic information system ofclaim 8, wherein the report includes an analysis of the water bodywithin the polygon of at least one of the depth data points and thecoordinate data points.
 12. The geographic information system of claim8, wherein the parameter data points describe one of water temperature,barometric pressure, plant height, plant biovolume, area covered byplants, biomass, substrate hardness/softness, and bottomhardness/softness.
 13. A method of processing geo-statistical data, themethod comprising: receiving by a server from a water body monitoringsystem a first plurality of depth data points that represent values of awater body depth, a second plurality of coordinate data points thatrepresent a plurality of coordinates from which the first plurality ofdepth data points were measured, and a third plurality of water bodyparameter data points; aligning by the server the first plurality ofdepth data points with the second plurality of coordinate data points;extracting by the server the first plurality of depth data points andthe second plurality of coordinate data points; creating by the server afirst contour map from the first plurality of depth data points and thesecond plurality coordinate data points; creating a closed polygon onthe first contour map; and analyzing the third plurality of water bodyparameter data points within the polygon.
 14. The method of claim 13,and further comprising: analyzing at least one of the first plurality ofdepth data points and the second plurality of coordinate data pointswithin the polygon.
 15. The method of claim 13, wherein the thirdplurality of parameter data points represent one of water temperature,plant height, vegetation biovolume, substrate hardness, and bottomhardness.
 16. The method of claim 13, wherein the polygon is comprisedof a plurality of straight edges.
 17. The method of claim 13, wherein auser determines a plurality of edges of the polygon.